In this article, we describe the design and implementation of a publicly accessible dermatology image analysis benchmark challenge. The goal of the challenge is to sup- port research and development of algorithms for automated diagnosis of melanoma, a lethal form of skin cancer, from dermoscopic images. The challenge was divided into sub-challenges for each task involved in image analysis, including lesion segmentation, dermoscopic feature detection within a lesion, and classification of melanoma. Training data included 900 images. A separate test dataset of 379 images was provided to measure resultant performance of systems developed with the training data. Ground truth for both training and test sets was generated by a panel of dermoscopic experts. In total, there were 79 submissions from a group of 38 participants, making this the largest standardized and comparative study for melanoma diagnosis in dermoscopic images to date. While the official challenge duration and ranking of participants has concluded, the datasets remain available for further research and development.

本文章介绍了一项公共皮肤科图像分析挑战赛的设计与实现，其目的是支持自动诊断致命皮肤癌症麻风疹的算法的研究与开发。通过划分子挑战来分别完成图像分析任务，包括皮损分割、皮损内皮肤镜特征检测和黑色素瘤分类。最终统计共有79组参赛选手，38名参赛者，是迄今为止在皮损镜下诊断黑色素瘤的最大标准化和比较研究之一。

Skin Lesion Analysis toward Melanoma Detection: A Challenge at the
  International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the
  International Skin Imaging Collaboration (ISIC)

面部皮损分析以助于黑色素瘤检测： 由国际皮肤成像合作组织（ ISIC ）主办的2016年国际生物医学成像研讨会上的挑战

面部皮损分析以助于黑色素瘤检测：由国际皮肤成像合作组织（ ISIC ）主办的2016年国际生物医学成像研讨会上的挑战